Learning and generalization of motor skills by learning from demonstration

2009

Conference Paper

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We provide a general approach for learning
robotic motor skills from human demonstration. To represent
an observed movement, a non-linear differential equation is
learned such that it reproduces this movement. Based on this
representation, we build a library of movements by labeling
each recorded movement according to task and context (e.g.,
grasping, placing, and releasing). Our differential equation is
formulated such that generalization can be achieved simply by
adapting a start and a goal parameter in the equation to the
desired position values of a movement. For object manipulation,
we present how our framework extends to the control of gripper
orientation and finger position. The feasibility of our approach
is demonstrated in simulation as well as on a real robot. The
robot learned a pick-and-place operation and a water-serving
task and could generalize these tasks to novel situations.

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Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems